IoT Contextually-aware Digital Twin with Enhanced Discovery

ABSTRACT

IoT devices within a commercial real-estate or residential building environment may be connected through networks, such as a Building Automation and Control network (BACnet). Systems and methods according to this disclosure provide automatic discovery of IoT devices and relationships in commercial real-estate and residential buildings and integration of the BACnet devices into the digital twin of the building. In some implementations, an IoT gateway is configured to translate the communication received from the BACnet to an IoT cloud platform and configured to normalize the data across the different security platforms into a consistent format which enables integration and interoperability of the different building system platforms that may otherwise be operating in isolation from each other. Systems and methods according to the present disclosure provide edge based analytics and control of IoT BACnet devices based on defining conditions and rules, and provide integration of multiple building systems in the context of commercial real-estate and residential buildings.

This application is a continuation of U.S. patent application Ser. No.16/456,525 filed Jun. 28, 2019. The present application claims priorityto and the benefit of the above-identified application and theabove-identified application is incorporated by reference herein intheir entirety

FIELD

Aspects described herein generally relate to building automation, IoT(Internet of Things) devices, and hardware and software related thereto.More specifically, one or more aspects describe herein provideintelligent commercial real-estate and residential building systems andautomation.

BACKGROUND

IoT devices within a commercial real-estate building environment may beconnected through networks with different wired or wirelesscommunication protocols, such as Ethernet or Building Automation andControl network (BACnet), which is a wired communications protocol thatallows the building automation and control systems to communicate backup to a network. The IoT devices within a commercial real-estatebuilding environment may be connected to the Internet via an IoTGateway. The IoT Gateway bridges IoT BACnet devices with the Internet byperforming a communication protocol translation between the BACnet andInternet.

SUMMARY

The following presents a simplified summary of various aspects describedherein. This summary is not an extensive overview, and is not intendedto identify required or critical elements or to delineate the scope ofthe claims. The following summary merely presents some concepts in asimplified form as an introductory prelude to the more detaileddescription provided below.

According to one aspect, the disclosure relates to acomputer-implemented method of generating a contextually-aware digitaltwin of a commercial real estate or residential building. The methodincludes receiving information about an asset comprising one or moredevices residing in one or more physical spaces associated with thecommercial real-estate or residential building. The method includesdetermining based on an identification of the one or more devices of theasset, one or more points corresponding to a sensor or an actuator ofthe one or more devices. The method includes identifying, based on theone or more points, at least one controller that serves the asset viathe one or more points. The at least one controller comprises a physicalor logical entity. The method includes determining, one or morenetworks, and one or more locations associated with the asset. Themethod includes storing each of the asset, the one or more points, theat least one controller, the one or more networks, and the one or morelocations as separate nodes of a graph representation of the commercialreal estate building.

In some implementations, the method includes determining, based on anidentification of a device connected to a network, whether the device isstored in the graph representation of the commercial real estate orresidential building. In some implementations, the method includes,based on a determination that the device is not stored in the graphdatabase of the commercial real estate building, identifying the deviceas a new device. In some implementations, the method includesdetermining one or more points corresponding to a sensor or an actuatorof the new device. In some implementations, the method includesidentifying, based on the one or more points of the new device, at leastone controller, at least one location, and at least one network that isassociated with the new device. In some implementations, the methodincludes storing the one or more points, the at least one controller,the at least one location, and the at least one network associated withthe new device in the graph representation of the commercial real-estatebuilding. In some implementations, the one or more devices of the assetare connected to a Building Automation Control (BAC) network. In someimplementations, the one or more locations may include a physical orlogical location associated with the one or more physical spaces of thecommercial real estate building. The one or more locations comprise atleast one of an identification of a floor, a longitude-latitudecoordinate, a space, an elevation, a zone, a space zone, and anidentification of an occupant of the one or more physical spaces. Insome implementations, the graph representation of the building comprisesa label property graph representation. In some implementations, thepoints comprise at least one of a setpoint value or a schedule. In someimplementations, the identifying of the at least one controllerassociated with the new device is based on information generated bytraining a machine learning algorithm on one or more previouslygenerated graph representations of the commercial real estate building.

According to certain aspects of the present disclosure, a system forgenerating a contextually-aware digital twin of a commercial real estatebuilding is provided. The system includes one or more processors. Thesystem includes a memory storing computer-readable instructions that,when executed by the one or more processors, cause the one or moreprocessors to receive information about an asset comprising one or moredevices residing in one or more physical spaces associated with thecommercial real estate building. The instructions cause the one or moreprocessors to determine based on an identification of the one or moredevices of the asset, one or more points corresponding to a sensor or anactuator of the one or more devices. The instructions cause the one ormore processors to identify, based on the one or more points, at leastone controller that serves the asset via the one or more points. The atleast one controller is a physical or logical entity. The instructionscause the one or more processors to determine, one or more networks, andone or more locations associated with the asset. The instructions causethe one or more processors to store each of the asset, the one or morepoints, the at least one controller, the one or more networks, and theone or more locations as separate nodes of a graph representation of thecommercial real estate building.

In some implementations, the instructions cause the one or moreprocessors to, determine, based on an identification of a deviceconnected to a network, whether the device is stored in the graphrepresentation of the commercial real estate building. In someimplementations, the instructions cause the one or more processors to,based on a determination that the device is not stored in the graphdatabase of the commercial real estate building, identify the device asa new device. In some implementations, the instructions cause the one ormore processors to determine one or more points corresponding to asensor or an actuator of the new device. In some implementations, theinstructions cause the one or more processors to identify, based on theone or more points of the new device, at least one controller, at leastone location, and at least one network that is associated with the newdevice. In some implementations, the instructions cause the one or moreprocessors to store the one or more points, the at least one controller,the at least one location, and the at least one network associated withthe new device in the graph representation of the commercial real-estatebuilding. In some implementations, the one or more devices of the assetare connected to a Building Automation Control (BAC) network. In someimplementations, the one or more locations may include a physical orlogical location associated with the one or more physical spaces of thecommercial real estate building. The one or more locations comprise atleast one of an identification of a floor, a longitude-latitudecoordinate, a space, an elevation, a zone, a space zone, and anidentification of an occupant of the one or more physical spaces. Insome implementations, the graph representation of the building comprisesa label property graph representation. In some implementations, thepoints comprise at least one of a setpoint value or a schedule. In someimplementations, the identifying of the at least one controllerassociated with the new device is based on information generated bytraining a machine learning algorithm on one or more previouslygenerated graph representations of the commercial real estate building.

According to certain aspects of the present disclosure, a non-transitorymachine readable storage medium containing machine-readable instructionsfor causing a processor to execute a method of generating acontextually-aware digital twin of a commercial real estate building isprovided. The instructions contained on the non-transitory machinereadable storage medium cause the processor to perform a method thatincludes receiving information about an asset comprising one or moredevices residing in one or more physical spaces associate with thecommercial real estate building. The method includes determining basedon an identification of the one or more devices of the asset, one ormore points corresponding to a sensor or an actuator of the one or moredevices. The method includes identifying, based on the one or morepoints, at least one controller that serves the asset via the one ormore points. The at least one controller comprises a physical or logicalentity. The method includes determining, one or more networks, and oneor more locations associated with the asset. The method includesstoring, each of the asset, the one or more points, the at least onecontroller, the one or more networks, and the one or more locations asseparate nodes of a graph representation of the commercial real estatebuilding.

These and additional aspects will be appreciated with the benefit of thedisclosures discussed in further detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of aspects described herein and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIG. 1 depicts an illustrative environment in which an IoTcontextually-aware digital twin with enhanced discovery is provided inaccordance with one or more illustrative aspects described herein.

FIG. 2 shows hardware elements of a computing device that may be used toimplement any of the computing devices shown in FIG. 1.

FIG. 3 illustrates an example process for providing an IoTcontextually-aware digital twin with enhanced discovery performed by theexample system shown in FIG. 1.

FIG. 4 illustrates another example process for providing an IoTcontextually-aware digital twin with enhanced discovery performed by theexample system shown in FIG. 1.

FIG. 5 illustrates another example process for providing IoT edgeanalytics and control performed by the example system shown in FIG. 1.

FIG. 6 illustrates an example graph schema of various entities andrelationships.

FIG. 7 illustrates an example graph schema of the location entities.

FIG. 8 illustrates an example representation of an actor system in anactor model.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings identified above and which form a parthereof, and in which is shown by way of illustration various embodimentsin which aspects described herein may be practiced. It is to beunderstood that other embodiments may be utilized and structural andfunctional modifications may be made without departing from the scopedescribed herein. Various aspects are capable of other embodiments andof being practiced or being carried out in various different ways.

It is to be understood that the phraseology and terminology used hereinare for the purpose of description and should not be regarded aslimiting. Rather, the phrases and terms used herein are to be giventheir broadest interpretation and meaning. The use of “including” and“comprising” and variations thereof is meant to encompass the itemslisted thereafter and equivalents thereof as well as additional itemsand equivalents thereof. The use of the terms “mounted,” “connected,”“coupled,” “positioned,” “engaged” and similar terms, is meant toinclude both direct and indirect mounting, connecting, coupling,positioning, and engaging.

One technique for creating digital twins of commercial real-estatebuildings relies on modeling the relationships between differentphysical entities, such as the building, floors, suites, and spaces, asparent-child relationships which, however, do not accurately capture themany-to-many relationships between these entities. As an example, avariable air volume (VAV) box connected to a VAV controller may servefour different spaces within a building. In a parent-child relationshipmodel, the VAV box is at the parent level, the VAV controller is a childof the VAV box, and the four spaces within the building are children ofthe VAV controller. As another example, there may be four VAV boxesserving a single open space. In this example, one parent-childrepresentation may have the single open space at the parent level withthe 4 VAV boxes as children. In another parent-child representation,each VAV box may be a parent node with the open space as its child node.Thus, modeling the relationships between different physical entities ina commercial building as parent-child relationships can become complexand convoluted. However, the many-to-many relationships between physicalentities in a commercial building can be modeled and stored as a graphdatabase. Some techniques use a RDF version of a graph database whichstores the relationships between entities in subject-predicate-objectformat, and thus, cannot go as deep as, for example, a label propertyversion of a graph database. Additionally, one technique does notrepresent controllers and devices as separate entities in the graphdatabase.

IoT devices within a commercial real-estate building environment may beconnected through networks with different wired or wirelesscommunication protocols, such as Ethernet or Building Automation andControl network (BACnet) which is a wired communications protocol thatallows the building automation and control systems to communicate backup to a network. The IoT devices within a commercial real-estatebuilding environment may be connected to the Internet via an IoTGateway. The IoT Gateway bridges IoT BACnet devices with the Internet byperforming a communication protocol translation between the BACnet andInternet. An IoT Gateway may monitor BACnet devices by periodicallypolling these devices. While a pubset model has been developed so thatIoT devices connected through a BACnet may publish in an asynchronousfashion, the pubset model is limited by the bandwidth of the BACnet,which has a specific baud rate and thus, there is only so much data thatcan be pumped onto the wire.

Some techniques use centralized queues for receiving data from devicesor sending data to devices, which are a single point of failure, havebottlenecks, and do not prioritize messages well. Additionally, acentralized scheduler does not account for the device response time, thenumber of devices that can be queried within a time interval, or theaggregation of queries over time intervals (i.e. queries over 5 second,10 second, 15 second intervals stacking on top of each other). Further,a centralized rules engine (i.e. if temperature exceeds value ‘y’, thenchange to mode value ‘z’) requires too much complexity to account fordynamic rules per device.

Systems and methods according to this disclosure provide acontextually-aware digital twin of a commercial building based on agraph database representation of entities and relationships that areimportant in the context of commercial buildings and spaces. Everycommercial building is unique. Even though two commercial buildings maylook alike, the connectivity between a device and its controller may bedifferent between the buildings. Thus, the same device may have adifferent relationship to its controller in each building. Further, adevice and its controller may be physically separate entities. A devicesuch as a thermostat may be placed at a specific location because thatmay be the best location for the thermostat to accurately determine thetemperature within a space. However, the thermostat may be communicatingwith a controller that is in a different location from the thermostat.As another example, a variable air volume (VAV) box is an essential partof an air conditioning system in any large industrial or commercialbuilding. The air conditioning system is, in turn, part of a HVACsystem, which encompasses heating, ventilation, air conditioning, andassociated control systems. The VAV box may connect to a VAV controllerthat controls the opening and shutting of the VAV box dials in order tocontrol the flow of air that is coming into a building. The VAV box mayalso include a heating element. If the air entering into the building isnot at a high enough temperature, the VAV controller may turn on theheating element in the VAV box to raise the temperature of the airentering the building. In complex commercial buildings and structures,the VAV controller is not necessarily synonymous with the VAV box ordevice. Instead, the VAV box and the VAV controller may be physicallyseparate entities. Additionally, the VAV box and the VAV controller maybe manufactured by different entities. A customer may mix and match theparts from different manufacturers. Thus, modeling the VAV controllerand VAV box as different entities in the digital twin of the building isalso useful for inventory tracking. Therefore, decoupling the thermostatand its controller into separate entities in the graph database providesan accurate IoT model of the building resulting in accurate queries andanalytics. Thus, implementations according to this disclosure decoupledevices and controllers in terms of how the cloud platform interactswith them. Implementations may store document level data by using thelabel property graph version of the graph database which store all orsome of the properties of the device and points, which may be theregisters of a controller of a device. The digital twin of a buildingmay reside in the cloud and may track every device that is supported bythe platform. The digital twin may track points, assets, controllers andnetwork and location, including service location and installed location.Digital twins of different buildings may be integrated. The graphdatabase and actor model allows us to integrate with other digital twinslike BIM, energy models, third-party APIs, third-party building softwareand applications, etc. Additionally, relationships between entities maybe modeled based on an understanding of how the actual physicalequipment gets connected. For example, a controller may be managed byanother upstream controller, thus, the data model's representation ofparent-child relationships can scale. Further, implementations accordingto this disclosure enable data from traditionally separate subsystems tobe combined. Implementations enable search and filtering and navigationbased on location, type, etc. (e.g. all assets serving a specificroom—and query power meter, HVAC, lighting, etc.). An energy model istypically a static model that gets created upon initial design and usesapproximations for how systems will perform with a defined set ofinputs. In some implementations, an energy model may be calibrated forHVAC zones based on a representation of a building's digital twin.Implementations according to this disclosure may leverage machinelearning in which the inputs may be varied to model how the buildingwill perform in an infinite number of scenarios.

Systems and methods according to this disclosure provide automaticdiscovery of IoT devices and relationships in commercial real-estatebuildings and integration of the devices into the digital twin of thebuilding. Devices may be discovered on the network based on their IPaddresses. For example, BACnet devices may be discovered via the “whois”command which returns the device identifiers of BACnet devices. Forexample, there may be multiple different manufacturers that make VABboxes on the market and each manufacturer may manufacture severaldifferent models. This information may be recorded in a library ofdevices. When a new device is discovered on the network, it may beidentified based on the information recorded in the device library.Additionally, the information in the library of devices may includerelationships that may arise from an asset that includes a known device.The relationships for a device may be determined based on training amachine learning algorithm on the digital twins of various commercialreal-estate buildings. If a device is known, then the relationships thatcome from that device may be predicted, enabling automatic creation ofall or some of the related entities and relationships, therebyautomatically setting up the nodes and relationships of an asset in thedigital twin.

Systems and methods according to this disclosure provide an IoT Gatewayconfigured to translate the communication received from the BACnet to anIoT cloud platform. Systems and methods according to this disclosureprovide an IoT Gateway configured to provide normalization of big datagenerated by interconnected IoT in commercial real-estate buildings. Acontroller may have multiple registers in which the controller storesdata. Each register may be identified as a separate point. However,different manufacturers may store specific types of data in the sameregister. Therefore, in order to enable seamless data collection andanalytics, the data generated by the IoT devices and controllers may benormalized across various manufacturers and models before transmittingto a cloud platform for further processing, storage, and integrationwith other systems and platforms. The normalization may be performed atthe API level. For example, a typical commercial real-estate buildingmay comprise IoT devices and controllers related to multiple differentsecurity platforms that each store data in a specific manner or format.The IoT Gateway may be configured to normalize the data across thedifferent security platforms into a consistent format which enablesintegration and interoperability of the different building systemplatforms that may otherwise be operating in isolation from each other.Additionally, the IoT Gateway may be configured to evaluate and filterthe data received from the IoT devices before transmitting the data to acloud platform for further processing and analytics, thus reducing theimpact on network processing and bandwidth. Further, the IoT Gateway maybe configured to perform edge based analytics and edge based control ofIoT devices, including IoT BACnet devices, within a commercialreal-estate building environment. In some implementations, the IoTGateway may be configured to control the IoT devices based on evaluatingincoming real-time data with rules. The IoT Gateway may be configured toperform queries and analytics based on edge computing which does notrely on network connectivity or the cloud computing resources. Thisenables real-time response to events without significant increase inbandwidth or processing power.

Systems and methods according to the present disclosure provide edgebased analytics and control of IoT devices based on defining conditionsand rules. For example, a rule may trigger an action based on receivingreal-time data related to a supply air temperature that has exceeded amin/max alarm threshold. A windowing function may ensure that a specificnumber of consecutive real-time data points are received or observedbefore the rule is triggered. Once it is confirmed that the incomingdata points sufficiently meet the relevant conditions set by the rule,automatic control of the related devices may be triggered.Representations of building systems and devices may be created using anactor model. In an actor model, a plurality of actors may receivemessage from other actors, perform computations, manage their state, andsend messages to other actors. Actors may correspond to devices orgroups of devices related to building systems, such as HVAC, lighting,etc. For example, a sensor may communicate a measurement, such astemperature, to an actor corresponding to the sensor. The measurementmay be communicated to the actor as a message. Upon receiving themessage, the actor updates the stored state based on the sensormeasurement. The actor representing the sensor may also receive messagesfrom other actors for access to the latest sensor measurement. The actormay also send messages to the physical sensor device to update thedevice configuration. An actor that triggers a rule may query connecteddevices and assets to find an optimum solution based on the currentsituation of the system. Once data related to various solutions isreceived, the best option may be determined based on pre-determinedconstraints and settings, such as energy usage, time required, etc. Insome implementations, device analytics may be provided to model expectedperformance of a device based on a current state, and the device may becontrolled based on computations in real-time using context aware data.By maintaining a stateful machine representation of the device, theexact behavior of a device may be modeled by training a data analyticsmachine learning algorithm on the real-time data of the input and outputdata points of the device. Implementations may provide precision commandand control for an infinite number of stateful scenarios of the deviceinstead of just a handful that are included in the device's onboardcontroller.

The actor model may be implemented by a distributed computing andnon-blocking framework, such as Akka. The actor model and rules may bestored in a cloud platform. In addition to hosting an actor system inthe cloud, implementations may also run the actor system at the edgewhich avoids reliance on cloud computing and enables better real-timeresponse. The edge device analytics may be performed on an IoT gateway.

Systems and methods according to this disclosure provide integration ofmultiple building systems in the context of commercial real-estatebuildings. Typically, there may be multiple different building softwareplatforms supporting a commercial real-estate building that are isolatedfrom each other. Implementations according to this disclosure provideintegration of previously isolated software platforms and relatedbuilding systems. Integration of various building systems enablediscovery of new patterns and insights into the building systems.Integration of various building systems also enables the automation ofinteractions between devices across various building systems.

FIG. 1 depicts an illustrative environment 100 in which an IoTcontextually-aware digital twin with enhanced discovery is provided inaccordance with one or more illustrative aspects described herein. Theenvironment 100 includes three buildings 102 a-c (generally referred toas buildings 102). The buildings 102 may be commercial real-estate orindustrial buildings or structures. FIG. 1 shows an example building 102a. The example building 102 a includes eight devices 110 a-h (generallyreferred to as devices 110). The devices 110 may include electrical ormechanical devices associated with building automation systems (BAS) forapplications such as heating, ventilating, and air-conditioning (HVAC),lighting control, access control, and fire detection. The devices 110may include IoT “smart” devices. The devices 110 may include sensors andactuators. The devices 110 may be connected to one or more controllers.The example building 102 a includes four controllers 113 a-d (generallyreferred to as controllers 113). The controllers 113 may receiveinformation, such as sensor data, from the devices 110. The controllers113 may be configured to effectuate an action via the actuators in thedevices 110 based on evaluating the received sensor data. Referring tothe example building 102 a, the first and second devices 110 a-b areconnected to the first controller 113 a. The third and fourth devices110 c-d are connected to the second controller 113 b. Additionally, thesecond controller 113 b and a fifth device 110 are connected to a thirdcontroller 113 c. In the example building 102 a, the first through fifthdevices 110 a-e may be referred to as “BACnet devices” because they areconnected through a Building Automation and Control network (BACnet),which is a wired communications protocol that allows communication ofbuilding automation and control systems to transmit or receive data.

In addition to the devices 110 a-e that are connected to the BACnet 112,the example building 102 a includes a sixth device 110 f and a seventhdevice 110 g that are connected to a fourth controller 113 d. The sixthand seventh devices 110 f-g may communicate via an Ethernet protocol andmay be referred to as “Ethernet devices.” The example building 102 aincludes an IoT Gateway 125 which serves as a connection point betweenthe devices 110 and an external network, such as network 160. The BACnetdevices 110 a-d may connect to the IoT Gateway 125 via a BACnetInterface 112. The Ethernet devices may connect to the IoT Gateway viaan Ethernet Interface 120. An eighth device 110 h interfaces directlywith the IoT Gateway 125. The devices 110 may leverage other networkingprotocols. The example building 102 a may include additional devices 110that may communicate via communications protocols, such as CoAP, DTLSMQTT, ModBus, AMQP, HTTP, HTTPS, and FTP, among others. The examplebuilding 102 a may include additional devices 110 that may communicatevia wireless protocols, such as IPv6, LPWAN, Zigbee, Bluetooth LowEnergy, Z-Wave, RFID and NFC.

The IoT Gateway 125 may connect the devices 110 and the controllers 113to the Intelligent Building Cloud Platform 165 over a network 160. TheIoT Gateway 125 may be configured to bridge the BACnet devices 110 withthe Internet by performing a communication translation between theBACnet and Internet communication protocols. The IoT Gateway 125 maycommunicate with an Intelligent Building Edge Application Platform 142.Additionally, the IoT Gateway 125 may be configured to evaluate andfilter the data received from the IoT devices before transmitting thedata to a cloud platform for further processing and analytics, thusreducing the impact on network processing and bandwidth. Further, theIoT Gateway 125 may be configured to perform edge based analytics andedge based control of IoT devices 110, including the BACnet devices 110,within the example building 102 a. In some implementations, the IoTGateway 125 may be configured to control the IoT devices 110 based onevaluating incoming real-time data with rules. The IoT Gateway 125 maybe configured to perform queries and analytics based on edge computingwhich does not rely on network connectivity or the cloud computingresources. This enables real-time response to events without significantincrease in bandwidth or processing power.

The Intelligent Building Edge Application Platform 142 include anIntelligent Building Edge Application Server 140 via a communicationlink 130. The Intelligent Building Edge Application Server 140 mayreceive data via the IoT Gateway 125 and send the data over the network160 to the Intelligent Building Cloud Platform 165 for furtherprocessing and storage. The Intelligent Building Edge Application Server140 may push one or more applications to configure the IoT Gateway 125to perform various functions. Intelligent Building Edge ApplicationServer 140 may be configured to normalize the data generated by thedevices 110 and controller 113 may be normalized across variousmanufacturers and models before transmitting to a cloud platform, suchas the Intelligent Building Cloud Application Platform 165, for furtherprocessing, storage, and integration with other systems and platforms.The normalization may be performed at the API level. The IoT Gateway 125of different buildings may collect data specific for that building andsend the collected data to the Intelligent Building Cloud ApplicationServer which in turn process and analyze the data further for providingrecommendations. For example, implementations may include training amachine learning algorithm on the data collected across variousbuildings to gain insights and patterns which may then in turn beanalyzed for the purpose of providing recommendations on otherbuildings. Data from various buildings may be crowd sourced in suchimplementations. The Intelligent Building Edge Application Server 140may be configured to create and manipulate a contextually-aware digitaltwin for each of the buildings 102 based on a graph databaserepresentation of entities and relationships that are important in thecontext of commercial buildings and spaces. The Intelligent BuildingEdge Application Server 140 may be configured to provide automaticdiscovery of devices located in the buildings 102, generate the variousrelationships arising from the discovered devices and integrate thediscovered devices and relationships into the digital twins of thebuildings 102.

The Intelligent Building Edge Application Server 140 may be configuredto perform edge based analytics and control the devices 110. TheIntelligent Building Edge Application Server 140 may be configured togenerate and maintain an actor model comprising representations ofbuilding systems and devices. Actors may correspond to devices or groupsof devices related to building systems, such as HVAC, lighting, etc. TheIntelligent Building Edge Application Server 140 may store the generateddigital twins and actor models of the buildings 102 in the IntelligentBuilding Cloud Platform 165. The Intelligent Building Edge ApplicationServer 140 may also store the digital twins and the actor models of thebuildings 102 at the edge as Building Edge Data 145. The IntelligentBuilding Edge Application Server 140 may be configured provideintegration of multiple software platforms related to the buildingsystems of the buildings 102 and to enable discovery of new patterns andinsights into the building systems. The integration may enable theautomation of interactions between the devices 110 across variousbuilding systems of the building 102 a and across the buildings 102.

An example Building Edge Data 145 a includes an Edge Digital Twin 150and an Edge Actor Model 155. The Intelligent Building Edge ApplicationServer 140 may use the Building Edge Data 145 for performing edgeanalytics and processing. The Intelligent Building Cloud Platform 165includes an Intelligent Building Cloud Application Server 170 that maybe configured to perform cloud analytics and processing. The IntelligentBuilding Cloud Platform 165 includes a Device Library 190 and BuildingData 175. The Building Data 175 may store various information about thebuildings 102. An example Building Data 175 a includes a Historical Data185, a Digital Twin 180, and an Actor Model 182. The Historical Data 185may include data about one of the buildings 102 that is collected over aperiod of time. The Intelligent Building Cloud Application Server 170may access the Historical Data 185, the Digital Twin 180, and the ActorModel 182 in order to perform analytics that provide additional insightsinto the devices and systems of the corresponding building 102 a. TheIntelligent Building Cloud Application Server 170 may store informationabout devices and controllers in the Device Library 190. The IoT Gateway125 may include a Dell 500 box with a Linux operating system, and theIntelligent Building Edge Application Server 140 may include an IoTiumorchestrator using an orchestration system, such as Kubernetes. TheIntelligent Building Edge Application Server 140 may include softwareapplications developed in JavaScript or TypeScript using a Backstacklibrary, and an akkajs or scalajs framework. Additionally, PostgreSQLdatabase system may be used for relational data, and a message brokersuch as Kafka, Aka Stream, RabbitMO, EventHub, etc. The IntelligentBuilding Cloud Application Platform 165 may include Azure data storageand analytics.

FIG. 2 shows hardware elements of a computing device 200 that may beused to implement any of the computing devices shown in FIG. 1 (e.g.,any of the devices shown in the example building 102 a, any of thedevices shown in the Intelligent Building Cloud Application Platform165, any devices interfacing with the external network 160) and anyother computing devices discussed herein (e.g., the Intelligent BuildingCloud Application Server 170 and the Intelligent Building EdgeApplication Server 140 in FIG. 1). The computing device 200 may compriseone or more processors 201, which may execute instructions of a computerprogram to perform any of the functions described herein. Theinstructions may be stored in read-only memory (ROM) 202, random accessmemory (RAM) 203, removable media 204 (e.g., a USB drive, a compact disk(CD), a digital versatile disk (DVD)), and/or in any other type ofcomputer-readable medium or memory. Instructions may also be stored inan attached (or internal) hard drive 205 or other types of storagemedia. The computing device 200 may comprise one or more output devices,such as a display device 206 (e.g., an external television and/or otherexternal or internal display device) and a speaker 214, and may compriseone or more output device controllers 207, such as a video processor.One or more user input devices 208 may comprise a remote control, akeyboard, a mouse, a touch screen (which may be integrated with thedisplay device 206), microphone, etc. The computing device 200 may alsocomprise one or more network interfaces, such as a network input/output(I/O) interface 210 (e.g., a network card) to communicate with anexternal network 209. The network I/O interface 210 may be a wiredinterface (e.g., electrical, RF (via coax), optical (via fiber)), awireless interface, or a combination of the two. The network I/Ointerface 210 may comprise a modem configured to communicate via theexternal network 209. The external network 209 may comprise thecommunication links 101 discussed above, the external network 109, anin-home network, a network provider's wireless, coaxial, fiber, orhybrid fiber/coaxial distribution system (e.g., a DOCSIS network), orany other desired network. The computing device 200 may comprise alocation-detecting device, such as a global positioning system (GPS)microprocessor 211, which may be configured to receive and processglobal positioning signals and determine, with possible assistance froman external server and antenna, a geographic position of the computingdevice 200.

Although FIG. 2 shows an example hardware configuration, one or more ofthe elements of the computing device 200 may be implemented as softwareor a combination of hardware and software. Modifications may be made toadd, remove, combine, divide, etc. components of the computing device200. Additionally, the elements shown in FIG. 2 may be implemented usingbasic computing devices and components that have been configured toperform operations such as are described herein. For example, a memoryof the computing device 200 may store computer-executable instructionsthat, when executed by the processor 201 and/or one or more otherprocessors of the computing device 200, cause the computing device 200to perform one, some, or all of the operations described herein. Suchmemory and processor(s) may also or alternatively be implemented throughone or more Integrated Circuits (ICs). An IC may be, for example, amicroprocessor that accesses programming instructions or other datastored in a ROM and/or hardwired into the IC. For example, an IC maycomprise an Application Specific Integrated Circuit (ASIC) having gatesand/or other logic dedicated to the calculations and other operationsdescribed herein. An IC may perform some operations based on executionof programming instructions read from ROM or RAM, with other operationshardwired into gates or other logic. Further, an IC may be configured tooutput image data to a display buffer.

FIG. 3 illustrates an example process 300 for providing an IoTcontextually-aware digital twin with enhanced discovery performed by theexample system shown in FIG. 1. While FIG. 3 is described with referenceto FIG. 1, it should be noted that the method steps of FIG. 3 may beperformed by other systems.

A “thing” in the context of IoT (Internet of Things) may be a deviceconfigured to transmit or receive data through a network. Thus, a“thing” may be a sensor device transmitting sensor data to a controllerand receiving control data from the controller. An IoTcontextually-aware digital twin of a commercial real-estate building maybe generated based on an IoT data model schema that defines variousentities associated with a “thing”, such as a type, network,manufacturer, controller, point, asset, and location. A type entity maybe a classification of a thing. A thing may be classified based ondifferent categories, such as its class and subclass. A network entitymay be a collection of “things” that are connected through wired orwireless protocols. The wired protocol may be a BACnet protocol. Amanufacturer entity may be a maker or producer of a “thing”. A point maybe a sensor or actuator of a device or controller that contains adigital or analog property. A point may be a configuration value, suchas a setpoint value or a schedule. An asset entity may be physicalequipment or an object that is located within a place, as understood bya person having skill in the art after review of the entirety disclosedherein. An asset may comprise multiple physical devices and/or points.An asset may have attributes, such as a serial number, or amanufacturer. Assets may be categorized into types and subtypes. Assettypes may correspond to building systems, such as HVAC air, HVAC water,plumbing, fire protection, electrical, and fire alarm. Each asset typemay include various subtypes. An asset type corresponding to the HVACair system may include subtypes, such as the variable air volume (VAV)box/controller, fan powered box (FPB), fan coil unit (FCU), computerroom air handler (CRAH), fan system, fan, air handling unit (AHU), heatrecovery unit (HRU), and heat pump. An asset type corresponding to theHVAC water system may include subtypes, such as chilled water plant,pump, heat exchanger, unit heater, baseboard heater, duct heater,boiler, and expansion tank. An asset type corresponding to the plumbingsystem may include subtypes, such as pump, and grease trap. An assettype corresponding to the fire protection system may include subtypes,such as pump, and pre-action. An asset type corresponding to theelectrical system may include subtypes, such as meter, switchboard,panel board, generator, transfer switch, uninterruptible power supply(UPS), lighting control panel, fire alarm control panel (FACP), andremote control panel. A controller entity may be a physical or logicalentity that is associated with one or more points. A controller entitymay serve one or more assets through the points of the one or moreassets. A location entity may be a physical or logical identification ofthe whereabouts of a “thing,” such as longitude-latitude coordinates,elevation, address, floor name, logical, or physical zone. The locationentities may be determined based on the physical location of an asset,as well as the areas of the building that the asset serves. For example,a VAV box residing at one location may serve many other areas of thebuilding. Location entities may have their own digital twin that definesrelationships between the physical and logical locations. The digitaltwin of the location entities may interface with the digital twin of theasset.

The IoT data model schema enables physical devices 110 to be decoupledfrom their corresponding physical controllers 113 and represented asseparate entities in the digital twin of the building 102 a. Even thoughthe buildings 102 may look alike, the connectivity between a device,such as a sensor device, and its controller may be different across thebuildings 102. Thus, the same device 110 may have a differentrelationship to its controller 113 in each of the buildings 102.Further, a device and its controller may be physically separateentities. A device, such as a thermostat residing at a physicallocation, may communicate with a controller that is placed in adifferent physical location from the thermostat. The IoT data modelschema enables the thermostat and its controller to be represented asdifferent entities in the digital twin of the building. According to theIoT data model schema, the sensors and actuators of the thermostat maybe mapped to one or more points, and its physical controller may bemapped to a controller entity. As another example, in complex commercialbuildings and structures, a VAV controller may not be synonymous withthe VAV box or device that it controls. Instead, the VAV box and its VAVcontroller may be physically separate entities. Additionally, the VAVbox and its VAV controller may have different manufacturers, so that acustomer may mix and match the parts from different manufacturers. Inthis case, representing the VAV controller and VAV box as differententities in the digital twin is also useful for inventory tracking.Therefore, according the IoT data model schema, the sensors andactuators of the VAV box may be mapped to points and the VAV controllermay be mapped to a controller entity. In some cases, a device mayinclude sensors, actuators, and control logic within the same physicaldevice. According to the IoT data model schema, the sensors, actuators,and control logic of the device may be mapped to points and a logicalcontroller entity, and thus decoupled from each other and represented asseparate entities in the digital twin of the building. Decouplingdevices from their corresponding controllers and representing them asseparate entities provides a more accurate digital twin of the buildingresulting in more accurate querying and analytics.

FIG. 6 illustrates an example graph schema 600 of the various entitiesand relationships. The graph schema 600 includes a plurality of nodes605 a-g (generally referred to as nodes 605). The asset, type, point,location, controller, network, and manufacturer entities are representedrespectively as the nodes 605 a-g of the graph schema 600. The graphschema 600 includes a plurality of edges 610 a-i (generally referred toas edges 610). The edges 610 represent relationships between the nodes605 and their corresponding entities. The edges may be directional andmay include properties. The relationship between the asset entity ofnode 605 a and the type entity of node 605 b is represented as an edge610 a. The direction of the edge 610 a is from the asset entity of node605 a to the type entity of node 605 b. The property of the edge 610 aindicates that the asset entity of node 605 a is a “part of” the typeentity of node 605 b. The nodes 605 may include properties. The property620 a of the asset entity of node 605 a includes an “id” in a GUIDformat and a “name” in a string format. The relationship between theasset entity of node 605 a and the point entity of node 605 c isrepresented as an edge 610 b. The direction of the edge 610 b is fromthe asset entity of node 605 a to the point entity of node 605 c. Theproperty of the edge 610 b indicates that the point entity of node 605 c“belongs to” the asset entity of node 605 a. The property of the pointentity of node 605 c includes an “id” in a GUID format and a “name” in astring format. The relationship between the asset entity of node 605 aand the location entity of node 605 d is represented as the edge 610 c.The direction of the edge 610 c is to the asset entity of node 605 afrom the location entity of node 605 d. The property of the edge 610 cindicates that the asset entity of node 605 a is “served by” thelocation entity of node 605 d. The property 620 c of the point entity ofnode 605 c includes an “id” in a GUID format and a “name” in a stringformat. The relationship between the type entity of node 605 b and thepoint entity of node 605 c is represented as the edge 610 d. Thedirection of the edge 610 d is from the point entity of node 605 c tothe type entity of node 605 b. The property of the edge 610 d indicatesthat the point entity of node 605 c is “part of” the type entity of node605 b. The relationship between the location entity of node 605 d andthe point entity of node 605 c is represented as the edge 610 e. Thedirection of the edge 610 e is from the point entity of node 605 c tothe location entity of node 605 d. The property of the edge 610 eindicates that the point entity of node 605 c is “located in” thelocation entity of node 605 d. The relationship between the controllerentity of node 605 e and the type entity of node 605 b is represented asthe edge 610 f The direction of the edge 610 f is from the controllerentity of node 605 e to the type entity of node 605 b. The property ofthe edge 610 f indicates that the controller entity of node 605 e is“part of” the type entity of node 605 b. The relationship between thecontroller entity of node 605 e and the network entity of node 605 f isrepresented as the edge 610 g. The direction of the edge 610 g is fromthe controller entity of node 605 e to the network entity of node 605 fThe property of the edge 610 g indicates that the controller entity ofnode 605 e “resides on” the network entity of node 605 f. Therelationship between the controller entity of node 605 e and themanufacturer entity of node 605 g is represented as the edge 610 i. Thedirection of the edge 610 i is from the controller entity of node 605 eto the manufacturer entity of node 605 g. The property of the edge 610 iindicates that the controller entity of node 605 e is “made by” themanufacturer entity of node 605 g. The relationship between the locationentity of node 605 d and the network entity of node 605 f is representedas the edge 610 h. The direction of the edge 610 h is to the locationentity of node 605 d from the network entity of node 605 f. The propertyof the edge 610 h indicates that the network entity of node 605 f islocated in, as understood by a person of skill in the art after reviewof the entirety disclosed herein, the location entity of node 605 f.

FIG. 7 illustrates an example graph schema 700 of the location entities.Each of the location entities, such as tower, building, floor, company,space zone, space, and zone, correspond to a logical or physicallocation associated with the building. The tower, building, floor,company, space zone, space, and zone are represented respectively as thenodes 705 a-g (generally referred to as nodes 705) of the graph 700. Thenodes 705 may include properties 720 a-g, such as as an “id” in a GUIDformat, a “name” in a string format, and a “description” in a stringformat. The graph representation 700 includes a plurality of edges 710a-g that represent relationships between the location entities mapped tothe nodes 705. The relationship between the tower entity of node 705 aand the building entity of node 705 b is represented by the edge 710 a.The direction of the edge 710 a is from the tower entity of node 705 ato the building entity of node 705 b. The property of the edge 710 aindicates that the tower entity of node 705 a is a “located in” thebuilding entity of node 705 b. The relationship between the tower entityof node 705 a and the floor entity of node 705 c is represented by theedge 710 b. The direction of the edge 710 b is to the tower entity ofnode 705 a from the floor entity of node 705 c. The property of the edge710 b indicates that the floor entity of node 705 c is a “located in”the tower entity of node 705 a. The relationship between the buildingentity of node 705 b and the floor entity of node 705 c is representedby the edge 710 c. The direction of the edge 710 c is to the buildingentity of node 705 b from the floor entity of node 705 c. The propertyof the edge 710 c indicates that the floor entity of node 705 c is a“located in” the building entity of node 705 b. The relationship betweenthe building entity of node 705 b and the company entity of node 705 dis represented by the edge 710 d. The direction of the edge 710 d is tothe building entity of node 705 b from the company entity of node 705 d.The property of the edge 710 d indicates that the company entity of node705 d is a “located in” the building entity of node 705 b. Therelationship between the space zone entity of node 705 e and the companyentity of node 705 d is represented by the edge 710 e. The direction ofthe edge 710 e is from the space zone entity of node 705 e to thecompany entity of node 705 d. The property of the edge 710 e indicatesthat the space zone entity of node 705 d is a “occupied by” the companyentity of node 705 d.

Referring back to FIG. 1, the example building 102 a may include one ormore building systems. Each building system may include one or moreassets. As discussed above, an asset is an entity that is associatedwith a “thing.” An asset may be physical equipment or an object that islocated within a place. An asset may be associated with multiplephysical devices 110 and/or points. An asset may also includeattributes, such as a serial number, or a manufacturer. At stage 305,the process 300 begins when the Intelligent Building Edge ApplicationServer 140 receives information about an asset associated with acommercial real-estate building, such as the example building 102 a inFIG. 1. The asset may be associated with a building system of acommercial real-estate building. The information about the asset mayinclude an identification of one or more devices 110 that are associatedwith the asset.

A plurality of relationships associated with the asset may be identifiedbased on determining the points, controller entities, location entities,and network entities associated with the asset. The Device Library 190in the Intelligent Building Cloud Platform 165 may include variousinformation about the relationships between devices and controllersgenerated based on training a machine learning algorithm on the data ofvarious building systems. The plurality of relationships associated withthe asset may be determined based on the points, controller entities,location entities, and network entities associated with the asset.Therefore, at stage 310, the process 300 may include determining one ormore points associated with the asset. At stage 315, the process 300 mayinclude determining one or more controller entities associated with theasset. As discussed above, the controller entities may be logicalcontrollers or physical controllers, such as the controllers 113. Atstage 320, the process 300 may include determining a plurality oflocation entities for the asset. The location entities may be physicaland logical locations associated with the asset. The location entitiesmay be determined based on information about the actual locations of thedevices and controllers associated with the asset, the areas of thebuilding that the devices and controllers service (service areas), andother information from Building Information Modeling (BIM) data of thebuilding. At stage 325, the process 300 may include determining thenetwork entities associated with the asset. At stage 330, the process300 may include determining the upstream and downstream utilities ordata flow associated with the asset, thereby identifying the controllersand devices that are associated with the upstream and downstream dataflow from the asset.

In some implementations, the Intelligent Building Edge ApplicationServer 140 may be configured to provide an application and UserInterface (UI) for adding an asset. The determined relationshipsassociated with the asset may be validated in real-time based on assettemplates. The asset templates may be based on training a machinelearning algorithm on various building data and the digital twins ofvarious buildings.

The process 300 may be repeated for each asset, resulting in a digitaltwin of the commercial real-estate building. The Intelligent BuildingEdge Application Server 140 may send the generated digital twin to theIntelligent Building Cloud Application Server 170 of the IntelligentBuilding Cloud Platform 165 over the network 160. The IntelligentBuilding Cloud Application Server 170 may store the digital twin as theDigital Twin 180 in the Building Data 175. The Intelligent BuildingCloud Platform 165 may perform cloud analytics and processing based onintegrating the data stored in the Digital Twin 180 of the differentbuildings 102. The Intelligent Building Cloud Application Server 170 mayaccess the Historical Data 185, the Digital Twin 180, and the ActorModel 182 in order to perform analytics that provide additional insightsinto the devices and systems of the corresponding building 102 a. TheIntelligent Building Edge Application Server 140 may also store thedigital twins of the buildings 102 at the edge as Building Edge Data145. An example Building Edge Data 145 a includes an Edge Digital Twin150 and an Edge Actor Model 155. The Intelligent Building EdgeApplication Server 140 may use the Building Edge Data 145 for performingedge analytics and processing.

FIG. 4 illustrates another example process 400 for providing an IoTcontextually-aware digital twin with enhanced discovery performed by theexample system shown in FIG. 1. At stage 402, the process 402 mayinclude monitoring for a new device connected to a network. The networkmay be a BAC network. A command may be issued on the network to detectnew devices. The command may be a “whois” command on a BAC network. Thecommand may retrieve the device identifiers of devices connected to thenetwork. At stage 403, the process 400 detects a new device. The deviceidentifiers may indicate a new device that is connect to the network. Atstage 405, the new device is identified and the device characteristicsmay be determined. At stage 401, the process 400 includes determiningone or more points of the new device. The one or more points of the newdevice may be determined based on information stored in the DeviceLibrary 190. The Device Library 190 in the Intelligent Building CloudPlatform 165 may include various information about the relationshipsbetween devices and controllers generated based on training a machinelearning algorithm on the data of various building systems. At stage415, the process 400 may include identifying at least one controllerentity associated with the one or more points of the new device. Atstage 420, the process 400 includes determining the network and locationentities associated with the new device. At stage 425, the process 400may include updating the graph representation of the digital twin of thecommercial real-estate building with the points, networks, controllerand locations associated with the new device. At stage 435, the process400 may return to stage 402 monitor for a new device.

In some implementations, a User Interface (UI) may be provided foron-boarding building systems and devices. The Intelligent Building EdgeApplication Server 140 may be configured to provide an application andUser Interface (UI) for generating a contextually-aware digital twin ofa building. In order to access the application and UI, a user mayconnect a machine to a network of the building. The network mayinterface with a BACnet. The machine may be a laptop or an IoTium edgedevice. The user may provide information about a building, select abuilding from a list of buildings, and add all or some of the assetsassociated with the selected building to a database. The user may thenrun a command, such as a “whois” command, on the BACnet to discover thedevices on the network. In response, the device identifiers for each ofthe devices associated with each of the assets may be obtained. The usermay obtain additional details about the devices corresponding to thedevice identifiers by running a “whohas” command, on the BACnet. Basedon the device identifiers, the points corresponding to the devices maybe identified based on device information stored in, for example, theDevice Library 190. The points may be added to the assets and saved tothe database. The determined relationships associated with the asset maybe validated in real-time based on asset templates. The asset templatesmay be based on training a machine learning algorithm on variousbuilding data and the digital twins of various buildings. The assettemplates may be stored in a Document/NoSQL database that is integratedwith the graph database representation of the building. In addition tothe BACnet discovery discussed above, various information about thedevices, such as the physical location, may also be retrieved from .dwgformat files.

The graph representation of the building may track other staticinformation about devices such as a manufacturer, serial number, modelnumber, etc. Implementations may access the digital twin of a buildingto support user queries. For example, a user may, through an applicationon a user device, such as a smartphone, pull up data for the building orquery information about the values of devices. Since the edgeapplication is constantly updating the digital twin at the edge, it ispossible to quickly and efficiently search for various information aboutthe current state of the building systems.

FIG. 5 illustrates an example process 500 for providing IoT edgeanalytics and control performed by the example system shown in FIG. 1.While FIG. 5 is described with reference to FIG. 1, it should be notedthat the method steps of FIG. 5 may be performed by other systems.

Representations of building systems and devices may be created using anactor model, such as the Edge Actor Model 155. The actors may correspondto devices or groups of devices related to building systems, such asHVAC, lighting, etc. FIG. 8 illustrates an example actor system 800 inan actor model, such as the Edge Actor Model 155. For example, a sensormay communicate a real time measurement, such as temperature, to anactor corresponding to the sensor. The measurement may be communicatedto the actor corresponding to the sensor device as an Incoming Message880. The Incoming Message 880 is processed by a Processing module 840.The Processing module 840 may update the device state stored in theState 830 based on the sensor measurement received in the IncomingMessage 880. The actor 800 may also receive messages from other actorsfor access to the latest sensor measurement stored in the State 830. Theactor 800 may also send messages to the sensor device to update thedevice configuration. The data generated by each device is collected indifferent amounts and at different time intervals. The time interval isdependent on an importance of the device point for real-time control andanalytics. For example, a damper percentage may be designated as havinga medium priority, and thus data relevant for measuring the damperpercentage may be collected from the devices at a five minute interval.A zone temperature may be designated as having a high priority, and thusdata relevant for measuring the zone temperature may be collected fromthe relevant device points at a 30 second interval. An alarm may bedesignated as having a highest priority, and thus data relevant formeasuring triggering of the alarm may be collected in real-time based ona change of value. Since thousands of devices may be requesting data atdifferent intervals, the priority amongst devices needs to be maintainedin order to reduce overloading of the BACnet bandwidth. The actor modelmay be able to handle millions of messages per second. The actor modelis set up to mimic the device built in environment. In other words, theactor model maintains a stateful representation of the device. When thedata indicates a defective value within a set up window, the correctiveactions are triggered based on the rules in Rules 815. This enables aquicker response in real time without a need for cloud connectivity andreduces network bandwidth usage, response time, and manual input.

An actor that triggers a rule may query connected devices and assets tofind an optimum solution based on the current situation of the system.Once data related to various solutions is received, the best option maybe determined based on pre-determined constraints and settings, such asenergy usage, time required, etc. In some implementations, deviceanalytics may be provided to model expected performance of a devicebased on a current state, and the device may be controlled based oncomputations in real-time using context aware data. By maintaining astateful machine representation of the device, the exact behavior of adevice may be modeled by training a data analytics machine learningalgorithm on the real-time data of the input and output data points ofthe device. Implementations may provide precision command and controlfor an infinite number of stateful scenarios of the device instead ofjust a handful that are included in the device's onboard controller. Theactor model may be implemented by a distributed computing andnon-blocking framework, such as Akka. The actor model and rules may bestored as the Actor Model 182 in the Building Data 175 of theIntelligent Building Cloud Application Platform 165. In addition tohosting the Actor Model 182 in the cloud, implementations may also runthe actor system at the edge, which avoids reliance on cloud computingand enables better real-time response.

At stage 505, the process 500 includes setting up the rules for each ofthe actors. The Intelligent Building Edge Application Server 140 and theIoT Gateway 125 may be configured to provide edge based analytics andcontrol of the devices 110 and the controllers 113 based on rules. Forexample, a rule may be set to trigger an action based on receivingreal-time data related to a supply air temperature that has exceeded amin/max alarm threshold. The rule may indicate that if the supply airtemperature falls below a minimum threshold value or exceeded a maximumthreshold value for more than five minutes, then actions must be takento adjust air temperature to a preferred temperature set as apredetermined value. The rule may be stored in the Rules 815 of theactor 800 corresponding to the temperature sensor.

At stage 510, the process 500 includes monitoring the incoming real-timedata from the temperature sensor device as an Incoming Message 880, andat stage 515, the process 500 includes processing the Incoming Message880 and evaluating whether the real-time data meets the rule stored inthe Rules 815. A windowing function may ensure that a specific number ofconsecutive real-time measurements are received from the temperaturesensor in order for the rule in the Rules 815 to be triggered. Upon adetermination that the supply air temperature has fallen below theminimum threshold value or exceeded the maximum threshold value for morethan five minutes, the process 500 moves to stage 520.

At stage 520, the process 520 includes requesting other actors forinformation for adjusting the air temperature to the predeterminedvalue. For example, other actors relevant for maintaining the air supplytemperature may include devices related to a chiller system, an FPB, orother devices configured to effectuate local changes that may impact theair supply temperature. The air temperature actor may send the requestto other actors via an Outgoing Message 855. The other actors mayreceive the Outgoing Message 855 and determine the actions their devicesmay take in order to achieve a solution, based on the current state oftheir devices. For example, the actor corresponding to the chiller maydetermine that operating the chiller for 5 min at 12 Kw is needed toadjust the air temperature to the preferred temperature. The actorcorresponding to the FPB (Fan Powered Box) may determine that operatingits devices for 2 min at 14 Kw is needed to adjust the air temperatureto the preferred temperature. The actor corresponding for local changesmay determine that operating its devices for 7 min at 9 Kw is needed toadjust the air temperature to the preferred temperature. Each actorsends this information as an Outgoing Message 855 to the air temperatureactor which receives the information as Incoming Messages 880 andprocesses the messages. At stage 503 of the process 500, the airtemperature actor may select a solution based on evaluating theinformation received from the other actors based on other considerationsrelated to, for example, time, energy, and feasibility. At stage 545,the process 500 includes executing the solution by notifying therelevant assets. The air temperature actor may notify the relevant actorto take an action for executing the solution proposed by the actor. Forexample, the air temperature actor may send an Outgoing Message 855 tonotify the chiller actor to operate the chiller for 5 minutes at 12 KW.The chiller actor receives the message from the air temperature actor asan Incoming Message 880. The chiller actor turns on the cooling systemin order to decrease the air temperature and notifies the airtemperature actor. At stage 555 of the process 500, the air temperatureactor monitors the air temperature based on continuing to receivereal-time temperature measurements. At stage 560, the process 500includes determining whether the air temperature meets the criteria setby the rule stored in the Rule 815. Upon a determination that the airtemperature has returned to the preferred temperature, at stage 565 ofthe process 500, the air temperature actor notifies the chiller toreturn to its normal operation.

Performing edge based device analytics (i.e. space temperature rate ofchange) may be used for controlling a device, for example, an airdamper, without relying upon cloud compute. Performing edge deviceanalytics relies on accessing the digital twin at the edge to perform acomputation rather than relying on the digital twin stored in the cloud.For example, a device may have a data point called space temperature,which may have a function that checks for the rate of change of thatvalue. If the rate of change exceeds a threshold consecutively for agiven time period, the air damper of the device closes. This enables thedevice to act quickly to an urgent fire related condition withoutrelying upon network connectivity to the cloud platform in order toprovide a command back to the device.

In some implementations, the data collected from the devices may bestored in a time series database. If a user is attempting to change thetemperature, or check on a current status of a VAV box, it isinefficient and expensive to query the cloud stored data. Thus,implementations provide information about the VAV box or other deviceswithin the most recent period, such as 24 hours. If, based on the mostrecent data, it is observed that the present behavior of the device isnot as expected, the cloud platform may be accessed for querying thecloud stored big data related to the device. This may happen based onboth the end user triggering or requesting the query or automaticallybased on preset parameters and thresholds. In some implementations,artificial intelligence and machine learning may be used to analyze andidentify anomalies at the same time as the data is being collected. Insome implementations, a time bounding may be set of the last 30 sec orother predetermined time period of the data flowing in or beingcollected. Based on observed patterns in the data, an alert may betriggered which in turn triggers an action to be performed by variousdevices or systems. Using machine learning by training a machinelearning algorithm on previously collected data enables implementationsto predict anomalies in the device data as it is being collected.Implementations may access collected data and evaluate it to gaininsights. The data that comes off of that could provide previouslyunknown insights that would not be possible to obtain using traditionalmethods. Additionally, implementations are able to maintain andmanipulate the state machine for entire subsystems.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are described asexample implementations of the following claims.

1. A method of generating a contextually-aware digital twin of acommercial real estate building comprising: receiving, by a computingdevice, received information about an asset comprising one or moreInternet of Things (IoT) devices residing in one or more physical spacesassociated with the commercial real estate building; constructing fromthe received information, by the computing device, a graphrepresentation of each of the assets for the commercial real estatebuilding; discovering a discovered IoT device connected to an associatednetwork; translating from a first communication protocol to an InternetProtocol, by the computing device, discovered information about thediscovered IoT device; determining from the translated discoveredinformation whether the discovered IoT device is stored in the graphrepresentation of the commercial real estate building; when thediscovered IoT device is not stored in the graph representation,identifying a discovered point, a discovered controller, and adiscovered location associated with the discovered IoT device; andmodifying the graph representation in to include the discovered pointthe discovered controller, the discovered location, and the associatednetwork.
 2. The method of claim 1, further comprising: translating froma second communication protocol to the Internet Protocol, by thecomputing device, the received information about the asset.
 3. Themethod of claim 2, wherein the first and second communication protocolsare different.
 4. The method of claim 1, further comprising:determining, based on an identification of a device connected to anetwork, whether the device is stored in the graph representation of thecommercial real estate building; based on a determination that thedevice is not stored in a graph database of the commercial real estatebuilding, identifying the device as a new device; determining, one ormore points corresponding to a sensor or an actuator of the new device;identifying, based on the one or more points of the new device, at leastone controller, at least one location, and at least one network that isassociated with the new device; and storing the one or more points, theat least one controller, the at least one location, and the at least onenetwork associated with the new device in the graph representation ofthe commercial real estate building.
 5. The method of claim 4, whereinthe one or more devices of the asset are connected to a BuildingAutomation Control (BAC) network.
 6. The method of claim 4, wherein theat least one location comprises a physical or logical locationassociated with the one or more physical spaces of the commercial realestate building, and wherein the one or more locations comprise at leastone of an identification of a floor, a longitude-latitude coordinate, aspace, an elevation, a zone, a space zone, and an identification of anoccupant of the one or more physical spaces.
 7. The method of claim 4,wherein the graph representation of the building comprises a labelproperty graph representation.
 8. The method of claim 4, wherein thepoints comprise at least one of a setpoint or a schedule.
 9. The methodof claim 4, wherein the identifying of the at least one controllerassociated with the new device is based on information generated bytraining a machine learning algorithm on one or more previouslygenerated graph representations of the commercial real estate building.10. A system for generating a contextually-aware digital twin of acommercial real estate building comprising: one or more processors; anda memory storing computer-readable instructions that, when executed bythe one or more processors, configure the one or more processors to:receive received information about an asset comprising one or moreInternet of Things (IoT) devices residing in one or more physical spacesassociated with the commercial real estate building; construct from thereceived information a graph representation of each of the assets forthe commercial real estate building; discover a discovered IoT deviceconnected to an associated network; translate, from a firstcommunication protocol to an Internet Protocol, discovered informationabout the discovered IoT device; determine from the translateddiscovered information whether the discovered IoT device is stored inthe graph representation of the commercial real estate building; whenthe discovered IoT device is not stored in the graph representation,identify a discovered point, a discovered controller, and a discoveredlocation associated with the discovered IoT device; and modify the graphrepresentation to include the discovered point the discoveredcontroller, the discovered location, and the associated network.
 11. Thesystem of claim 10, wherein the memory storing computer-readableinstructions that, when executed by the one or more processors,configure the one or more processors to: translate, from a secondcommunication protocol to the Internet Protocol, the receivedinformation about the asset.
 12. The system of claim 10, wherein thememory storing computer-readable instructions that, when executed by theone or more processors, configure the one or more processors to:determine, based on an identification of a device connected to anetwork, whether the device is stored in the graph representation of thecommercial real estate building; based on a determination that thedevice is not stored in a graph database of the commercial real estatebuilding, identify the device as a new device; determine, one or morepoints corresponding to a sensor or an actuator of the new device;identify, based on the one or more points of the new device, at leastone controller, at least one location, and at least one network that isassociated with the new device; and store the one or more points, the atleast one controller, the at least one location, and the at least onenetwork associated with the new device in the graph representation ofthe commercial real estate building.
 13. The system of claim 12, whereinthe one or more devices of the asset are connected to a BuildingAutomation Control (BAC) network.
 14. The system of claim 12, whereinthe at least one location comprises a physical or logical locationassociated with the one or more physical spaces of the commercial realestate building, and wherein the one or more locations comprise at leastone of an identification of a floor, a longitude-latitude coordinate, aspace, an elevation, a zone, a space zone, and an identification of anoccupant of the one or more physical spaces.
 15. The system of claim 12,wherein the identifying of the at least one controller associated withthe new device is based on information generated by training a machinelearning algorithm on one or more previously generated graphrepresentations of the commercial real estate building.
 16. Anon-transitory machine readable storage medium comprisingmachine-readable instructions for causing a processor to execute amethod of generating a contextually-aware digital twin of a commercialreal estate building comprising: receiving, by a computing device,received information about an asset comprising one or more Internet ofThings (IoT) devices residing in one or more physical spaces associatedwith the commercial real estate building; constructing from the receivedinformation, by the computing device, a graph representation of each ofthe assets for the commercial real estate building; discovering adiscovered IoT device connected to an associated network; translating,from a first communication protocol to an Internet Protocol, discoveredinformation about the discovered IoT device; determining from thetranslated discovered information whether the discovered IoT device isstored in the graph representation of the commercial real estatebuilding; when the discovered IoT device is not stored in the graphrepresentation, identifying a discovered point, a discovered controller,and a discovered location associated with the discovered IoT device; andmodifying the graph representation to include the discovered point thediscovered controller, the discovered location, and the associatednetwork.
 17. The non-transitory machine readable storage medium of claim16, wherein the machine-readable instructions for causing the processorto execute the method comprising: translating, from a secondcommunication protocol to the Internet Protocol, the receivedinformation about the asset.
 18. The non-transitory machine readablestorage medium of claim 16, wherein the machine-readable instructionsfor causing the processor to execute the method comprising: determining,based on an identification of a device connected to a network, whetherthe device is stored in the graph representation of the commercial realestate building; based on a determination that the device is not storedin a graph database of the commercial real estate building, identifyingthe device as a new device; determining, one or more pointscorresponding to a sensor or an actuator of the new device; identifying,based on the one or more points of the new device, at least onecontroller, at least one location, and at least one network that isassociated with the new device; and storing the one or more points, theat least one controller, the at least one location, and the at least onenetwork associated with the new device in the graph representation ofthe commercial real estate building.
 19. The non-transitory machinereadable storage medium of claim 18, wherein the at least one locationcomprises a physical or logical location associated with the one or morephysical spaces of the commercial real estate building, and wherein theone or more locations comprise at least one of an identification of afloor, a longitude-latitude coordinate, a space, an elevation, a zone, aspace zone, and an identification of an occupant of the one or morephysical spaces.
 20. The non-transitory machine readable storage mediumof claim 18, wherein the identifying of the at least one controllerassociated with the new device is based on information generated bytraining a machine learning algorithm on one or more previouslygenerated graph representations of the commercial real estate building.